Publication
Errors in human-robot interactions and their effects on robot learning
Su-Kyoung Kim; Elsa Andrea Kirchner; Lukas Schloßmüller; Frank Kirchner
In: Frontiers in Robotics and AI, Vol. 7 - Section Computational Intelligence in Robotics, Pages Article-558531, Frontiers, 10/2020.
Abstract
During human-robot interaction, errors will occur. Hence, understanding the effects of interaction
errors and especially the effect of prior knowledge on robot learning performance is relevant
to develop appropriate approaches for learning under natural interaction conditions, since
future robots will continue to learn based on what they have already learned. In this study,
we investigated interaction errors that occurred under two learning conditions, i.e., in the case that
the robot learned without prior knowledge (cold-start learning) and in the case that the robot had
prior knowledge (warm-start learning). In our human-robot interaction scenario, the robot learns
to assign the correct action to a current human intention (gesture). Gestures were not predefined
but the robot had to learn their meaning. We used a contextual-bandit approach to maximize
the expected payoff by updating (a) the current human intention (gesture) and (b) the current
human intrinsic feedback after each action selection of the robot. As an intrinsic evaluation of the
robot behavior we used the error-related potential (ErrP) in the human electroencephalogram
as reinforcement signal. Either gesture errors (human intentions) can be misinterpreted by
incorrectly captured gestures or errors in the ErrP classification (human feedback) can occur. We
investigated these two types of interaction errors and their effects on the learning process. Our
results show that learning and its online adaptation was successful under both learning conditions
(except for one subject in cold-start learning). Furthermore, warm-start learning achieved faster
convergence, while cold-start learning was less affected by online changes in the current context.